In [1]:
######## snakemake preamble start (automatically inserted, do not edit) ########
import sys; sys.path.extend(['/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/site-packages', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024', '/home/ckikawa/.conda/envs/seqneut-pipeline/bin', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/lib-dynload', '/home/ckikawa/.local/lib/python3.11/site-packages', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/site-packages', '/home/ckikawa/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpy6xj203u/file/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks']); import pickle; snakemake = 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Sequencing-based neutralization assays of 2023-2024 human serum samples versus H3N2 influenza libraries\n\nThe numerical data and computer code are at 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from snakemake.logging import logger; logger.printshellcmds = False; import os; os.chdir(r'/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024');
######## snakemake preamble end #########

Process plate counts to get fraction infectivities and fit curves¶

This notebook is designed to be run using snakemake, and analyzes a plate of sequencing-based neutralization assays.

The plots generated by this notebook are interactive, so you can mouseover points for details, use the mouse-scroll to zoom and pan, and use interactive dropdowns at the bottom of the plots.

Setup¶

Import Python modules:

In [2]:
import pickle
import sys

import altair as alt

import matplotlib.pyplot as plt

import neutcurve

import numpy

import pandas as pd

import ruamel.yaml as yaml

_ = alt.data_transformers.disable_max_rows()

Get the variables passed by snakemake:

In [3]:
count_csvs = snakemake.input.count_csvs
fate_csvs = snakemake.input.fate_csvs
viral_library_csv = snakemake.input.viral_library_csv
neut_standard_set_csv = snakemake.input.neut_standard_set_csv
qc_drops_yaml = snakemake.output.qc_drops
frac_infectivity_csv = snakemake.output.frac_infectivity_csv
fits_csv = snakemake.output.fits_csv
fits_pickle = snakemake.output.fits_pickle
samples = snakemake.params.samples
plate = snakemake.wildcards.plate
plate_params = snakemake.params.plate_params

# get thresholds turning lists into tuples as needed
manual_drops = {
    filter_type: [tuple(w) if isinstance(w, list) else w for w in filter_drops]
    for (filter_type, filter_drops) in plate_params["manual_drops"].items()
}
group = plate_params["group"]
qc_thresholds = plate_params["qc_thresholds"]
curvefit_params = plate_params["curvefit_params"]
curvefit_qc = plate_params["curvefit_qc"]
curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"] = [
    tuple(w) for w in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
]

print(f"Processing {plate=}")

samples_df = pd.DataFrame(plate_params["samples"])
print(f"\nPlate has {len(samples)} samples (wells)")
assert all(
    (len(samples_df) == samples_df[c].nunique())
    for c in ["well", "sample", "sample_noplate"]
)
assert len(samples_df) == len(
    samples_df.groupby(["serum_replicate", "dilution_factor"])
)
assert len(samples) == len(count_csvs) == len(fate_csvs) == len(samples_df)

for d, key, title in [
    (manual_drops, "manual_drops", "Data manually specified to drop:"),
    (qc_thresholds, "qc_thresholds", "QC thresholds applied to data:"),
    (curvefit_params, "curvefit_params", "Curve-fitting parameters:"),
    (curvefit_qc, "curvefit_qc", "Curve-fitting QC:"),
]:
    print(f"\n{title}")
    yaml.YAML(typ="rt").dump({key: d}, stream=sys.stdout)

Processing plate='plate2'

Plate has 28 samples (wells)

Data manually specified to drop:
manual_drops: {}

QC thresholds applied to data:
qc_thresholds:
  avg_barcode_counts_per_well: 500
  min_neut_standard_frac_per_well: 0.005
  no_serum_per_viral_barcode_filters:
    min_frac: 0.0001
    max_fold_change: 4
    max_wells: 2
  per_neut_standard_barcode_filters:
    min_frac: 0.005
    max_fold_change: 4
    max_wells: 2
  min_neut_standard_count_per_well: 1000
  min_no_serum_count_per_viral_barcode_well: 100
  max_frac_infectivity_per_viral_barcode_well: 3
  min_dilutions_per_barcode_serum_replicate: 6

Curve-fitting parameters:
curvefit_params:
  frac_infectivity_ceiling: 1
  fixtop:
  - 0.6
  - 1
  fixbottom: 0
  fixslope:
  - 0.8
  - 10

Curve-fitting QC:
curvefit_qc:
  max_frac_infectivity_at_least: 0.0
  goodness_of_fit:
    min_R2: 0.5
    max_RMSD: 0.15
  serum_replicates_ignore_curvefit_qc: []
  barcode_serum_replicates_ignore_curvefit_qc: []

Set up dictionary to keep track of wells, barcodes, well-barcodes, and serum-replicates that are dropped:

In [4]:
qc_drops = {
    "wells": {},
    "barcodes": {},
    "barcode_wells": {},
    "barcode_serum_replicates": {},
    "serum_replicates": {},
}

assert set(manual_drops).issubset(
    qc_drops
), f"{manual_drops.keys()=}, {qc_drops.keys()}"

Statistics on barcode-parsing for each sample¶

Make interactive chart of the "fates" of the sequencing reads parsed for each sample on the plate.

If most sequencing reads are not "valid barcodes", this could potentially indicate some problem in the sequencing or barcode set you are parsing.

Potential fates are:

  • valid barcode: barcode that matches a known virus or neutralization standard, we hope most reads are this.
  • invalid barcode: a barcode with proper flanking sequences, but does not match a known virus or neutralization standard. If you have a lot of reads of this type, it is probably a good idea to look at the invalid barcode CSVs (in the ./results/barcode_invalid/ subdirectory created by the pipeline) to see what these invalid barcodes are.
  • unparseable barcode: could not parse a barcode from this read as there was not a sequence of the correct length with the appropriate flanking sequence.
  • invalid outer flank: if using an outer upstream or downstream region (upstream2 or downstream2 for the illuminabarcodeparser), reads that are otherwise valid except for this outer flank. Typically you would be using upstream2 if you have a plate index embedded in your primer, and reads with this classification correspond to a different index than the one for this plate.
  • low quality barcode: low-quality or N nucleotides in barcode, could indicate problem with sequencing.
  • failed chastity filter: reads that failed the Illumina chastity filter, if these are reported in the FASTQ (they may not be).

Also, if the number of reads per sample is very uneven, that could indicate that you did not do a good job of balancing the different samples in the Illumina sequencing.

In [5]:
fates = (
    pd.concat([pd.read_csv(f).assign(sample=s) for f, s in zip(fate_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .assign(
        fate_counts=lambda x: x.groupby("fate")["count"].transform("sum"),
        sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")",
    )
    .query("fate_counts > 0")[  # only keep fates with at least one count
        ["fate", "count", "well", "serum_replicate", "sample_well", "dilution_factor"]
    ]
)

assert len(fates) == len(fates.drop_duplicates())

serum_replicates = sorted(fates["serum_replicate"].unique())
sample_wells = list(
    fates.sort_values(["serum_replicate", "dilution_factor"])["sample_well"]
)


serum_selection = alt.selection_point(
    fields=["serum_replicate"],
    bind=alt.binding_select(
        options=[None] + serum_replicates,
        labels=["all"] + serum_replicates,
        name="serum",
    ),
)

fates_chart = (
    alt.Chart(fates)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X("count", scale=alt.Scale(nice=False, padding=3)),
        alt.Y(
            "sample_well",
            title=None,
            sort=sample_wells,
        ),
        alt.Color("fate", sort=sorted(fates["fate"].unique(), reverse=True)),
        alt.Order("fate", sort="descending"),
        tooltip=fates.columns.tolist(),
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=200,
        title=f"Barcode parsing for {plate}",
    )
    .configure_axis(grid=False)
)

fates_chart
Out[5]:

Read barcode counts and apply manually specified drops¶

Read the counts per barcode:

In [6]:
# get barcode counts
counts = (
    pd.concat([pd.read_csv(c).assign(sample=s) for c, s in zip(count_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .drop(columns=["replicate", "plate", "fastq"])
    .assign(sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")")
)

# classify barcodes as viral or neut standard
barcode_class = pd.concat(
    [
        pd.read_csv(viral_library_csv)[["barcode", "strain"]].assign(
            neut_standard=False,
        ),
        pd.read_csv(neut_standard_set_csv)[["barcode"]].assign(
            neut_standard=True,
            strain=pd.NA,
        ),
    ],
    ignore_index=True,
)

# merge counts and classification of barcodes
assert set(counts["barcode"]) == set(barcode_class["barcode"])
counts = counts.merge(barcode_class, on="barcode", validate="many_to_one")
assert set(sample_wells) == set(counts["sample_well"])
assert set(serum_replicates) == set(counts["serum_replicate"])

Apply any manually specified data drops:

In [7]:
for filter_type, filter_drops in manual_drops.items():
    print(f"\nDropping {len(filter_drops)} {filter_type} specified in manual_drops")
    assert filter_type in qc_drops
    qc_drops[filter_type].update(
        {w: "manual_drop" for w in filter_drops if not isinstance(w, list)}
    )
    if filter_type == "barcode_wells":
        counts = counts[
            ~counts.assign(
                barcode_well=lambda x: x.apply(
                    lambda r: (r["barcode"], r["well"]), axis=1
                )
            )["barcode_well"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "barcode_serum_replicates":
        counts = counts[
            ~counts.assign(
                barcode_serum_replicate=lambda x: x.apply(
                    lambda r: (r["barcode"], r["serum_replicate"]), axis=1
                )
            )["barcode_serum_replicate"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "wells":
        counts = counts[~counts["well"].isin(qc_drops[filter_type])]
    elif filter_type == "barcodes":
        counts = counts[~counts["barcode"].isin(qc_drops[filter_type])]
    else:
        assert filter_type in set(counts.columns)
        counts = counts[~counts[filter_type].isin(qc_drops[filter_type])]

Average counts per barcode in each well¶

Plot average counts per barcode. If a sample has inadequate barcode counts, it may not have good enough statistics for accurate analysis, and a QC-threshold is applied:

In [8]:
avg_barcode_counts = (
    counts.groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(avg_count=pd.NamedAgg("count", "mean"))
    .assign(
        fails_qc=lambda x: (
            x["avg_count"] < qc_thresholds["avg_barcode_counts_per_well"]
        ),
    )
)

avg_barcode_counts_chart = (
    alt.Chart(avg_barcode_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "avg_count",
            title="average barcode counts per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['avg_barcode_counts_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if avg_barcode_counts[c].dtype == float else c
            for c in avg_barcode_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Average barcode counts per well for {plate}",
    )
    .configure_axis(grid=False)
)

display(avg_barcode_counts_chart)

# drop wells failing QC
avg_barcode_counts_per_well_drops = list(avg_barcode_counts.query("fails_qc")["well"])
print(
    f"\nDropping {len(avg_barcode_counts_per_well_drops)} wells for failing "
    f"{qc_thresholds['avg_barcode_counts_per_well']=}: "
    + str(avg_barcode_counts_per_well_drops)
)
qc_drops["wells"].update(
    {w: "avg_barcode_counts_per_well" for w in avg_barcode_counts_per_well_drops}
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['avg_barcode_counts_per_well']=500: []

Fraction of counts from neutralization standard¶

Determine the fraction of counts from the neutralization standard in each sample, and make sure this fraction passess the QC threshold.

In [9]:
neut_standard_fracs = (
    counts.assign(
        neut_standard_count=lambda x: x["count"] * x["neut_standard"].astype(int)
    )
    .groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(
        total_count=pd.NamedAgg("count", "sum"),
        neut_standard_count=pd.NamedAgg("neut_standard_count", "sum"),
    )
    .assign(
        neut_standard_frac=lambda x: x["neut_standard_count"] / x["total_count"],
        fails_qc=lambda x: (
            x["neut_standard_frac"] < qc_thresholds["min_neut_standard_frac_per_well"]
        ),
    )
)

neut_standard_fracs_chart = (
    alt.Chart(neut_standard_fracs)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_frac",
            title="frac counts from neutralization standard per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_frac_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if neut_standard_fracs[c].dtype == float else c
            for c in neut_standard_fracs.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard fracs per well for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_fracs_chart)

# drop wells failing QC
min_neut_standard_frac_per_well_drops = list(
    neut_standard_fracs.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_frac_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_frac_per_well']=}: "
    + str(min_neut_standard_frac_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_frac_per_well"
        for w in min_neut_standard_frac_per_well_drops
    }
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['min_neut_standard_frac_per_well']=0.005: []

Consistency and minimum fractions for barcodes¶

We examine the fraction of counts attributable to each barcode. We do this splitting the data two ways:

  1. Looking at all viral (but not neut-standard) barcodes only for the no-serum samples (wells).

  2. Looking at just the neut-standard barcodes for all samples (wells).

The reasons is that if the experiment is set up perfectly, these fractions should be the same across all samples for each barcode. (We do not expect viral barcodes to have consistent fractions across no-serum samples as they will be neutralized differently depending on strain).

We plot these fractions in interactive plots (you can mouseover points and zoom) so you can identify barcodes that fail the expected consistency QC thresholds.

We also make sure the barcodes meet specified QC minimum thresholds for all samples, and flag any that do not.

In [10]:
barcode_selection = alt.selection_point(fields=["barcode"], on="mouseover", empty=False)

# look at all samples for neut standard barcodes, or no-serum samples for all barcodes
for is_neut_standard, df in counts.groupby("neut_standard"):
    if is_neut_standard:
        print(
            f"\n\n{'=' * 89}\nAnalyzing neut-standard barcodes from all samples (wells)"
        )
        qc_name = "per_neut_standard_barcode_filters"
    else:
        print(f"\n\n{'=' * 89}\nAnalyzing all barcodes from no-serum samples (wells)")
        qc_name = "no_serum_per_viral_barcode_filters"
        df = df.query("serum == 'none'")

    df = df.assign(
        sample_counts=lambda x: x.groupby("sample")["count"].transform("sum"),
        count_frac=lambda x: x["count"] / x["sample_counts"],
        median_count_frac=lambda x: x.groupby("barcode")["count_frac"].transform(
            "median"
        ),
        fold_change_from_median=lambda x: numpy.where(
            x["count_frac"] > x["median_count_frac"],
            x["count_frac"] / x["median_count_frac"],
            x["median_count_frac"] / x["count_frac"],
        ),
    )[
        [
            "barcode",
            "count",
            "well",
            "sample_well",
            "count_frac",
            "median_count_frac",
            "fold_change_from_median",
        ]
        + ([] if is_neut_standard else ["strain"])
    ]

    # barcode fails QC if fails in sufficient wells
    qc = qc_thresholds[qc_name]
    print(f"Apply QC {qc_name}: {qc}\n")
    fails_qc = (
        df.assign(
            fails_qc=lambda x: ~(
                (x["count_frac"] >= qc["min_frac"])
                & (x["fold_change_from_median"] <= qc["max_fold_change"])
            ),
        )
        .groupby("barcode", as_index=False)
        .aggregate(n_wells_fail_qc=pd.NamedAgg("fails_qc", "sum"))
        .assign(fails_qc=lambda x: x["n_wells_fail_qc"] >= qc["max_wells"])[
            ["barcode", "fails_qc"]
        ]
    )
    df = df.merge(fails_qc, on="barcode", validate="many_to_one")

    # make chart
    evenness_chart = (
        alt.Chart(df)
        .add_params(barcode_selection)
        .encode(
            alt.X(
                "count_frac",
                title=(
                    "barcode's fraction of neut standard counts"
                    if is_neut_standard
                    else "barcode's fraction of non-neut standard counts"
                ),
                scale=alt.Scale(nice=False, padding=5),
            ),
            alt.Y("sample_well", sort=sample_wells),
            alt.Fill(
                "fails_qc",
                title=f"fails {qc_name}",
                legend=alt.Legend(titleLimit=500),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".2g") if df[c].dtype == float else c
                for c in df.columns
            ],
        )
        .mark_circle(fillOpacity=0.45, stroke="black", strokeOpacity=1)
        .properties(
            height=alt.Step(10),
            width=300,
            title=alt.TitleParams(
                (
                    f"{plate} all samples, neut-standard barcodes"
                    if is_neut_standard
                    else f"{plate} no-serum samples, all barcodes"
                ),
                subtitle="x-axis is zoomable (use mouse scroll/pan)",
            ),
        )
        .configure_axis(grid=False)
        .configure_legend(titleLimit=1000)
        .interactive()
    )

    display(evenness_chart)

    # drop barcodes failing QC
    barcode_drops = list(fails_qc.query("fails_qc")["barcode"])
    print(
        f"\nDropping {len(barcode_drops)} barcodes for failing {qc=}: {barcode_drops}"
    )
    qc_drops["barcodes"].update(
        {bc: "min_neut_standard_frac_per_well" for bc in barcode_drops}
    )
    counts = counts[~counts["barcode"].isin(qc_drops["barcodes"])]


=========================================================================================
Analyzing all barcodes from no-serum samples (wells)
Apply QC no_serum_per_viral_barcode_filters: {'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}


Dropping 12 barcodes for failing qc={'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}: ['AAAGTAGCAGAGGATT', 'AAATTCACAATATCCA', 'AGACCATCGCACCCAA', 'ATAACGTTTGTGCAAA', 'CAAAAGCAGCACGATA', 'CATAAAAGACTGTATA', 'CGTACGTATGTCCCAG', 'CGTCCCTGGCGTGTCG', 'CGTTAACGGCCTATCC', 'TATATGGAATACTAAA', 'TCTCCGATAGCCCTAC', 'TGTTGTAATCTGAATA']


=========================================================================================
Analyzing neut-standard barcodes from all samples (wells)
Apply QC per_neut_standard_barcode_filters: {'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}


Dropping 0 barcodes for failing qc={'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}: []

Compute fraction infectivity¶

The fraction infectivity for viral barcode $v_b$ in sample $s$ is computed as: $$ F_{v_b,s} = \frac{c_{v_b,s} / \left(\sum_{n_b} c_{n_b,s}\right)}{{\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]} $$ where

  • $c_{v_b,s}$ is the counts of viral barcode $v_b$ in sample $s$.
  • $\sum_{n_b} c_{n_b,s}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for sample $s$.
  • $c_{v_b,s_0}$ is the counts of viral barcode $v_b$ in no-serum sample $s_0$.
  • $\sum_{n_b} c_{n_b,s_0}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for no-serum sample $s_0$.
  • ${\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]$ is the median taken across all no-serum samples of the counts of viral barcode $v_b$ versus the total counts for all neutralization standard barcodes.

First, compute the total neutralization-standard counts for each sample (well). Plot these, and drop any wells that do not meet the QC threshold.

In [11]:
neut_standard_counts = (
    counts.query("neut_standard")
    .groupby(
        ["well", "serum_replicate", "sample_well", "dilution_factor"],
        dropna=False,
        as_index=False,
    )
    .aggregate(neut_standard_count=pd.NamedAgg("count", "sum"))
    .assign(
        fails_qc=lambda x: (
            x["neut_standard_count"] < qc_thresholds["min_neut_standard_count_per_well"]
        ),
    )
)

neut_standard_counts_chart = (
    alt.Chart(neut_standard_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_count",
            title="counts from neutralization standard",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_count_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if neut_standard_counts[c].dtype == float
                else c
            )
            for c in neut_standard_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard counts for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_counts_chart)

# drop wells failing QC
min_neut_standard_count_per_well_drops = list(
    neut_standard_counts.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_count_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_count_per_well']=}: "
    + str(min_neut_standard_count_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_count_per_well"
        for w in min_neut_standard_count_per_well_drops
    }
)
neut_standard_counts = neut_standard_counts[
    ~neut_standard_counts["well"].isin(qc_drops["wells"])
]
counts = counts[~counts["well"].isin(qc_drops["wells"])]

Dropping 0 wells for failing qc_thresholds['min_neut_standard_count_per_well']=1000: []

Compute and plot the no-serum sample viral barcode counts and check if they pass the QC filters.

In [12]:
no_serum_counts = (
    counts.query("serum == 'none'")
    .query("not neut_standard")
    .merge(neut_standard_counts, validate="many_to_one")[
        ["barcode", "strain", "well", "sample_well", "count", "neut_standard_count"]
    ]
    .assign(
        fails_qc=lambda x: (
            x["count"] <= qc_thresholds["min_no_serum_count_per_viral_barcode_well"]
        ),
    )
)

strains = sorted(no_serum_counts["strain"].unique())
strain_selection_dropdown = alt.selection_point(
    fields=["strain"],
    bind=alt.binding_select(
        options=[None] + strains,
        labels=["all"] + strains,
        name="virus strain",
    ),
)

# make chart
no_serum_counts_chart = (
    alt.Chart(no_serum_counts)
    .add_params(barcode_selection, strain_selection_dropdown)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "count", title="viral barcode count", scale=alt.Scale(nice=False, padding=5)
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
        tooltip=no_serum_counts.columns.tolist(),
    )
    .mark_circle(fillOpacity=0.6, stroke="black", strokeOpacity=1)
    .properties(
        height=alt.Step(10),
        width=400,
        title=f"{plate} viral barcode counts in no-serum samples",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .interactive()
)

display(no_serum_counts_chart)

# drop barcode / wells failing QC
min_no_serum_count_per_viral_barcode_well_drops = list(
    no_serum_counts.query("fails_qc")[["barcode", "well"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_no_serum_count_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}: "
    + str(min_no_serum_count_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "min_no_serum_count_per_viral_barcode_well"
        for w in min_no_serum_count_per_viral_barcode_well_drops
    }
)
no_serum_counts = no_serum_counts[
    ~no_serum_counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]
counts = counts[
    ~counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]

Dropping 0 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: []

Compute and plot the median ratio of viral barcode count to neut standard counts across no-serum samples. If library composition is equal, all of these values should be similar:

In [13]:
median_no_serum_ratio = (
    no_serum_counts.assign(ratio=lambda x: x["count"] / x["neut_standard_count"])
    .groupby(["barcode", "strain"], as_index=False)
    .aggregate(median_no_serum_ratio=pd.NamedAgg("ratio", "median"))
)

strain_selection = alt.selection_point(fields=["strain"], on="mouseover", empty=False)

median_no_serum_ratio_chart = (
    alt.Chart(median_no_serum_ratio)
    .add_params(strain_selection)
    .encode(
        alt.X(
            "median_no_serum_ratio",
            title="median ratio of counts",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Y(
            "barcode",
            sort=alt.SortField("median_no_serum_ratio", order="descending"),
            axis=alt.Axis(labelFontSize=5),
        ),
        color=alt.condition(strain_selection, alt.value("orange"), alt.value("gray")),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if median_no_serum_ratio[c].dtype == float
                else c
            )
            for c in median_no_serum_ratio.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(5),
        width=250,
        title=f"{plate} no-serum median ratio viral barcode to neut-standard barcode",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(median_no_serum_ratio_chart)

Compute the actual fraction infectivities. We compute both the raw fraction infectivities and the ones with the ceiling applied:

In [14]:
frac_infectivity = (
    counts.query("not neut_standard")
    .query("serum != 'none'")
    .merge(median_no_serum_ratio, validate="many_to_one")
    .merge(neut_standard_counts, validate="many_to_one")
    .assign(
        frac_infectivity_raw=lambda x: (
            (x["count"] / x["neut_standard_count"]) / x["median_no_serum_ratio"]
        ),
        frac_infectivity_ceiling=lambda x: x["frac_infectivity_raw"].clip(
            upper=curvefit_params["frac_infectivity_ceiling"]
        ),
        concentration=lambda x: 1 / x["dilution_factor"],
        plate_barcode=lambda x: x["plate_replicate"] + "-" + x["barcode"],
    )[
        [
            "barcode",
            "plate_barcode",
            "well",
            "strain",
            "serum",
            "serum_replicate",
            "dilution_factor",
            "concentration",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
)

assert len(
    frac_infectivity.groupby(["serum", "plate_barcode", "dilution_factor"])
) == len(frac_infectivity)
assert frac_infectivity["dilution_factor"].notnull().all()
assert frac_infectivity["frac_infectivity_raw"].notnull().all()
assert frac_infectivity["frac_infectivity_ceiling"].notnull().all()

Plot the fraction infectivities, both the raw values and with the ceiling applied:

In [15]:
frac_infectivity_chart_df = (
    frac_infectivity.assign(
        fails_qc=lambda x: (
            x["frac_infectivity_raw"]
            > qc_thresholds["max_frac_infectivity_per_viral_barcode_well"]
        ),
    )
    .melt(
        id_vars=[
            "barcode",
            "strain",
            "well",
            "serum_replicate",
            "dilution_factor",
            "fails_qc",
        ],
        value_vars=["frac_infectivity_raw", "frac_infectivity_ceiling"],
        var_name="ceiling_applied",
        value_name="frac_infectivity",
    )
    .assign(
        ceiling_applied=lambda x: x["ceiling_applied"].map(
            {
                "frac_infectivity_raw": "raw fraction infectivity",
                "frac_infectivity_ceiling": f"fraction infectivity with ceiling at {curvefit_params['frac_infectivity_ceiling']}",
            }
        )
    )
)

frac_infectivity_chart = (
    alt.Chart(frac_infectivity_chart_df)
    .add_params(strain_selection_dropdown, barcode_selection)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "dilution_factor",
            title="dilution factor",
            scale=alt.Scale(nice=False, padding=5, type="log"),
        ),
        alt.Y(
            "frac_infectivity",
            title="fraction infectivity",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Column(
            "ceiling_applied",
            sort="descending",
            title=None,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold", labelPadding=2),
        ),
        alt.Row(
            "serum_replicate",
            title=None,
            spacing=3,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold"),
        ),
        alt.Detail("barcode"),
        alt.Shape(
            "fails_qc",
            title=f"fails {qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        color=alt.condition(
            barcode_selection, alt.value("black"), alt.value("MediumBlue")
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(3), alt.value(1)),
        opacity=alt.condition(barcode_selection, alt.value(1), alt.value(0.25)),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if frac_infectivity_chart_df[c].dtype == float
                else c
            )
            for c in frac_infectivity_chart_df.columns
        ],
    )
    .mark_line(point=True)
    .properties(
        height=150,
        width=250,
        title=f"Fraction infectivities for {plate}",
    )
    .interactive(bind_x=False)
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .configure_point(size=50)
    .resolve_scale(x="independent", y="independent")
)

display(frac_infectivity_chart)

# drop barcode / wells failing QC
max_frac_infectivity_per_viral_barcode_well_drops = list(
    frac_infectivity_chart_df.query("fails_qc")[["barcode", "well"]]
    .drop_duplicates()
    .itertuples(index=False, name=None)
)
print(
    f"\nDropping {len(max_frac_infectivity_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}: "
    + str(max_frac_infectivity_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "max_frac_infectivity_per_viral_barcode_well"
        for w in max_frac_infectivity_per_viral_barcode_well_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]

Dropping 180 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('GCTAATTCCAAAAGCG', 'D2'), ('TAGTTGCCCCGACCTG', 'D2'), ('TGATCCGCAAGCTTAG', 'D2'), ('GCTAATTCCAAAAGCG', 'D3'), ('CTGAAACCTTGTCCTA', 'D3'), ('CCAATAAAATACGATG', 'D3'), ('TTAGTCATCTGGGTGC', 'D3'), ('CCCCAGGTATAAAATA', 'D3'), ('ACTGAACAGTATAACT', 'D3'), ('TATCATTTCATCTACA', 'D3'), ('GTTATTATGACTTCAT', 'D3'), ('GAAAATCGAGCTTTAA', 'D3'), ('AAACTTCGTGGTATAC', 'D3'), ('AAAGATAAATTCAAAA', 'D3'), ('AACGACACTTACATCC', 'D3'), ('CTATCTTAATCTACAG', 'D3'), ('TCCAAACAGCGTTAAA', 'D3'), ('AATATACCGGCACTAC', 'D3'), ('TCAATGAATGCGGGGT', 'D3'), ('AGATCATAAGCAATAA', 'D3'), ('CCACGTTCATTAGATG', 'D3'), ('TATCGCAATATGATAA', 'D3'), ('CGCAAGGGATACTAAC', 'D3'), ('CGTCAGAAGTTTATAA', 'D3'), ('TCTTAACTACCCGATG', 'D3'), ('TTATGTTTTAATGGTA', 'D3'), ('AACAATTAATTTTTCA', 'D3'), ('TAGTTGCCCCGACCTG', 'D3'), ('GCTGGTGCACAAGATT', 'D3'), ('TGACAAACACCTGAGG', 'D3'), ('CAGCCGCTAAAATGAT', 'D3'), ('CCGCAATGACAATTTG', 'D3'), ('AATCTTTCCAATCTTG', 'D3'), ('CCAGAGACACGCTAGG', 'D3'), ('CCCGCTAACCCTGTCT', 'D3'), ('GCTAATTCCAAAAGCG', 'D4'), ('CCAATAAAATACGATG', 'D4'), ('CCACGTTCATTAGATG', 'D4'), ('AAACTTCGTGGTATAC', 'D4'), ('TTACGTCAATGTTTGA', 'D4'), ('AACAATTAATTTTTCA', 'D4'), ('CAAAAAGCTAATAAGT', 'D4'), ('TCGCGGTAGATTTGCG', 'D4'), ('TCTTAACTACCCGATG', 'D4'), ('CTCCAATAGGAGACGA', 'D4'), ('AATCTTTCCAATCTTG', 'D4'), ('AACCACCCCAGAGATG', 'D10'), ('GCTAATTCCAAAAGCG', 'G2'), ('GCATTATAATCTTGTG', 'G2'), ('TACCATTTTGGTCCGC', 'G2'), ('CCAATAAAATACGATG', 'G2'), ('TCTTAACTACCCGATG', 'G2'), ('AATCTTTCCAATCTTG', 'G2'), ('TAAAAGAATGATGGTC', 'G3'), ('CTGAAACCTTGTCCTA', 'G3'), ('ATCGAAAAAACTGCAA', 'G3'), ('TGTAGTATAAGAATAA', 'G3'), ('GCATTATAATCTTGTG', 'G3'), ('TATCAATTCGGTATTA', 'G3'), ('TACCATTTTGGTCCGC', 'G3'), ('TTGCTAGTCTACCTGA', 'G3'), ('GCTAATTCCAAAAGCG', 'G3'), ('AGTTATGTAAAACGTG', 'G3'), ('TTAGTCATCTGGGTGC', 'G3'), ('CCAATAAAATACGATG', 'G3'), ('GAAAATCGAGCTTTAA', 'G3'), ('AAAGATAAATTCAAAA', 'G3'), ('GTTATTATGACTTCAT', 'G3'), ('TATCATTTCATCTACA', 'G3'), ('ACGTAAATCCCCACAA', 'G3'), ('CCACAAGTTTGAAAAC', 'G3'), ('TAGCTGGGCAAAGGCT', 'G3'), ('TCAAACTATGATATTC', 'G3'), ('CTATCTTAATCTACAG', 'G3'), ('CAAGAAATGTAGTGAA', 'G3'), ('AAACTTCGTGGTATAC', 'G3'), ('TCCAAACAGCGTTAAA', 'G3'), ('GACAGAAACAAAATTA', 'G3'), ('TGCCGATCCAATTGAT', 'G3'), ('AATATACCGGCACTAC', 'G3'), ('TATCGCAATATGATAA', 'G3'), ('CCATCACCTTATACAC', 'G3'), ('CCACGTTCATTAGATG', 'G3'), ('CAAGACAAGCCCTATA', 'G3'), ('CGCAAGGGATACTAAC', 'G3'), ('TCTTAACTACCCGATG', 'G3'), ('TAGATAATAAGATTCA', 'G3'), ('TGGCTAGCGCACACCA', 'G3'), ('AACAATTAATTTTTCA', 'G3'), ('TCTTGAATTTCATGGA', 'G3'), ('CGTCAGAAGTTTATAA', 'G3'), ('ATGGTTATCTTACCTT', 'G3'), ('AATCTTTCCAATCTTG', 'G3'), ('CATAATGCACAAACGC', 'G3'), ('TAGTTGCCCCGACCTG', 'G3'), ('GCTGGTGCACAAGATT', 'G3'), ('CCGCAATGACAATTTG', 'G3'), ('CAATTCGCCGTTCCCC', 'G3'), ('TAAAAGAATGATGGTC', 'G4'), ('ATCGAAAAAACTGCAA', 'G4'), ('GCATTATAATCTTGTG', 'G4'), ('AGTTATGTAAAACGTG', 'G4'), ('TACCATTTTGGTCCGC', 'G4'), ('TTAGTCATCTGGGTGC', 'G4'), ('TATTATCTAAACGGCG', 'G4'), ('GCTAATTCCAAAAGCG', 'G4'), ('GTTATTATGACTTCAT', 'G4'), ('GACCAAAAAGCAGTAT', 'G4'), ('CCCCAGGTATAAAATA', 'G4'), ('CTGAAACCTTGTCCTA', 'G4'), ('GAAAATCGAGCTTTAA', 'G4'), ('CCAATAAAATACGATG', 'G4'), ('ACTGAACAGTATAACT', 'G4'), ('CAAGAAATGTAGTGAA', 'G4'), ('TAGCTGGGCAAAGGCT', 'G4'), ('TATCATTTCATCTACA', 'G4'), ('TCCAAACAGCGTTAAA', 'G4'), ('TCAAACTATGATATTC', 'G4'), ('AAACTTCGTGGTATAC', 'G4'), ('CCGATAAGACGTCGCT', 'G4'), ('CCACGTTCATTAGATG', 'G4'), ('CTATCTTAATCTACAG', 'G4'), ('TCAATGAATGCGGGGT', 'G4'), ('CGCAAGGGATACTAAC', 'G4'), ('TATCGCAATATGATAA', 'G4'), ('CTAAGGGCCTGTTCTT', 'G4'), ('AAGCGGTTTAGGTCCA', 'G4'), ('AATATACCGGCACTAC', 'G4'), ('CTGCGAATATTGTGAC', 'G4'), ('TAGATAATAAGATTCA', 'G4'), ('AGCATGAGCTTGTCAT', 'G4'), ('CAAAAAGCTAATAAGT', 'G4'), ('TCGCGGTAGATTTGCG', 'G4'), ('TAGTTGCCCCGACCTG', 'G4'), ('TCTTGAATTTCATGGA', 'G4'), ('TGGCTAGCGCACACCA', 'G4'), ('ATGGTTATCTTACCTT', 'G4'), ('GGTTGCGTAGTTAATC', 'G4'), ('CGTCAGAAGTTTATAA', 'G4'), ('TCTTAACTACCCGATG', 'G4'), ('AACAATTAATTTTTCA', 'G4'), ('ATCGATTCGATTGACG', 'G4'), ('GACCTCCTGGGCACGC', 'G4'), ('GCTGGTGCACAAGATT', 'G4'), ('CTCATTACAGAAATTG', 'G4'), ('TTATAATGGCCGGTAT', 'G4'), ('CCAGAGACACGCTAGG', 'G4'), ('AATCTTTCCAATCTTG', 'G4'), ('CCTATAAGGCCTTACG', 'G4'), ('GCATTATAATCTTGTG', 'G5'), ('CTGAAACCTTGTCCTA', 'G5'), ('TTAGTCATCTGGGTGC', 'G5'), ('TATTATCTAAACGGCG', 'G5'), ('AAGCTAATCGTAGTCC', 'G5'), ('GCTAATTCCAAAAGCG', 'G5'), ('CAAGAAATGTAGTGAA', 'G5'), ('CCAATAAAATACGATG', 'G5'), ('TAATAACTTGAGATTC', 'G5'), ('AACGACACTTACATCC', 'G5'), ('AATATACCGGCACTAC', 'G5'), ('TCAATGAATGCGGGGT', 'G5'), ('CCACGTTCATTAGATG', 'G5'), ('AAACTTCGTGGTATAC', 'G5'), ('TATCGCAATATGATAA', 'G5'), ('TAGATAATAAGATTCA', 'G5'), ('TCTTGAATTTCATGGA', 'G5'), ('AAGAAATTATGGCAGG', 'G5'), ('CGCAAGGGATACTAAC', 'G5'), ('CTGCGAATATTGTGAC', 'G5'), ('TGGCTAGCGCACACCA', 'G5'), ('AACAATTAATTTTTCA', 'G5'), ('TAGTTGCCCCGACCTG', 'G5'), ('GACCTCCTGGGCACGC', 'G5'), ('CCCGCTAACCCTGTCT', 'G5'), ('GCTAATTCCAAAAGCG', 'G6'), ('CGCAAGGGATACTAAC', 'G6'), ('TCTTAACTACCCGATG', 'G6'), ('TAGTTGCCCCGACCTG', 'G6'), ('AATCTTTCCAATCTTG', 'G6'), ('AATGACAGCTGTCTAG', 'G9')]

Check how many dilutions we have per barcode / serum-replicate:

In [16]:
n_dilutions = (
    frac_infectivity.groupby(["serum_replicate", "strain", "barcode"], as_index=False)
    .aggregate(**{"number of dilutions": pd.NamedAgg("dilution_factor", "nunique")})
    .assign(
        fails_qc=lambda x: (
            x["number of dilutions"]
            < qc_thresholds["min_dilutions_per_barcode_serum_replicate"]
        ),
    )
)

n_dilutions_chart = (
    alt.Chart(n_dilutions)
    .add_params(barcode_selection)
    .encode(
        alt.X("number of dilutions", scale=alt.Scale(nice=False, padding=4)),
        alt.Y("strain", title=None),
        alt.Column(
            "serum_replicate",
            title=None,
            header=alt.Header(labelFontSize=12, labelFontStyle="bold", labelPadding=0),
        ),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
        tooltip=[
            alt.Tooltip(c, format=".3g") if n_dilutions[c].dtype == float else c
            for c in n_dilutions.columns
        ],
    )
    .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.45)
    .properties(
        height=alt.Step(10),
        width=120,
        title=alt.TitleParams(
            "number of dilutions for each barcode for each serum-replicate", dy=-2
        ),
    )
)

display(n_dilutions_chart)

# drop barcode / serum-replicates failing QC
min_dilutions_per_barcode_serum_replicate_drops = list(
    n_dilutions.query("fails_qc")[["barcode", "serum_replicate"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_dilutions_per_barcode_serum_replicate_drops)} barcode/serum-replicates for failing "
    f"{qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}: "
    + str(min_dilutions_per_barcode_serum_replicate_drops)
)
qc_drops["barcode_serum_replicates"].update(
    {
        w: "min_dilutions_per_barcode_serum_replicate"
        for w in min_dilutions_per_barcode_serum_replicate_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_serum_replicate=lambda x: x.apply(
            lambda r: (r["barcode"], r["serum_replicate"]), axis=1
        )
    )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
]

Dropping 0 barcode/serum-replicates for failing qc_thresholds['min_dilutions_per_barcode_serum_replicate']=6: []

Fit neutralization curves without applying QC to curves¶

First fit curves to all serum replicates, then we will apply QC on the curve fits. Note that the fitting is done to the fraction infectivities with the ceiling:

In [17]:
fits_noqc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum_replicate",
    replicate_col="barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

Determine which fits fail the curve fitting QC, and plot them. Note the plot indicates as failing QC any barcode / serum-replicate that fails, even if we are also specified to ignore the QC for that one (so it will not be removed later):

In [18]:
goodness_of_fit = curvefit_qc["goodness_of_fit"]

fit_params_noqc = (
    frac_infectivity.groupby(["serum_replicate", "barcode"], as_index=False)
    .aggregate(max_frac_infectivity=pd.NamedAgg("frac_infectivity_ceiling", "max"))
    .merge(
        fits_noqc.fitParams(average_only=False, no_average=True)[
            ["serum", "virus", "replicate", "r2", "rmsd"]
        ].rename(columns={"serum": "serum_replicate", "replicate": "barcode"}),
        validate="one_to_one",
    )
    .assign(
        fails_max_frac_infectivity_at_least=lambda x: (
            x["max_frac_infectivity"] < curvefit_qc["max_frac_infectivity_at_least"]
        ),
        fails_goodness_of_fit=lambda x: (
            (x["r2"] < goodness_of_fit["min_R2"])
            & (x["rmsd"] > goodness_of_fit["max_RMSD"])
        ),
        fails_qc=lambda x: (
            x["fails_max_frac_infectivity_at_least"] | x["fails_goodness_of_fit"]
        ),
        ignore_qc=lambda x: x.apply(
            lambda r: (
                (
                    r["serum_replicate"]
                    in curvefit_qc["serum_replicates_ignore_curvefit_qc"]
                )
                or (
                    (r["barcode"], r["serum_replicate"])
                    in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
                )
            ),
            axis=1,
        ),
    )
)

print(f"Plotting barcode / serum-replicates that fail {curvefit_qc=}\n")

for prop, col in [
    ("max frac infectivity", "max_frac_infectivity"),
    ("curve fit R2", "r2"),
    ("curve fit RMSD", "rmsd"),
]:
    fit_params_noqc_chart = (
        alt.Chart(fit_params_noqc)
        .add_params(barcode_selection)
        .encode(
            alt.X(col, title=prop, scale=alt.Scale(nice=False, padding=4)),
            alt.Y("virus", title=None),
            alt.Fill("fails_qc"),
            alt.Column(
                "serum_replicate",
                title=None,
                header=alt.Header(
                    labelFontSize=12, labelFontStyle="bold", labelPadding=0
                ),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".3g") if fit_params_noqc[c].dtype == float else c
                for c in fit_params_noqc.columns
            ],
        )
        .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.55)
        .properties(
            height=alt.Step(10),
            width=120,
            title=alt.TitleParams(f"{prop} for each barcode serum-replicate", dy=-2),
        )
    )
    display(fit_params_noqc_chart)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

Plotting barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}

Now get all barcode / serum-replicate pairs that fail any of the QC. Plot curves for just these virus / serum-replicates (we plot all barcodes for a virus even if just one fails QC), and then exclude any that are not specified to ignore the QC:

In [19]:
barcode_serum_replicates_fail_qc = fit_params_noqc.query("fails_qc").reset_index(
    drop=True
)
print(f"Here are barcode / serum-replicates that fail {curvefit_qc=}")
display(barcode_serum_replicates_fail_qc)

if len(barcode_serum_replicates_fail_qc):
    print("\nCurves for viruses and serum-replicates with at least one failed barcode:")
    fig, _ = fits_noqc.plotReplicates(
        sera=sorted(barcode_serum_replicates_fail_qc["serum_replicate"].unique()),
        viruses=sorted(barcode_serum_replicates_fail_qc["virus"].unique()),
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=10,
        ticksize=10,
        ncol=6,
        draw_in_bounds=True,
    )
    display(fig)
    plt.close(fig)

# drop barcode / serum-replicates failing QC
for qc_filter in ["max_frac_infectivity_at_least", "goodness_of_fit"]:
    fits_qc_drops = list(
        fit_params_noqc.query(f"fails_{qc_filter} and (not ignore_qc)")[
            ["barcode", "serum_replicate"]
        ].itertuples(index=False, name=None)
    )
    print(
        f"\nDropping {len(fits_qc_drops)} barcode/serum-replicates for failing "
        f"{qc_filter}={curvefit_qc[qc_filter]}: " + str(fits_qc_drops)
    )
    qc_drops["barcode_serum_replicates"].update({w: qc_filter for w in fits_qc_drops})
    frac_infectivity = frac_infectivity[
        ~frac_infectivity.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]
    fit_params_noqc = fit_params_noqc[
        ~fit_params_noqc.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]

Here are barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}
serum_replicate barcode max_frac_infectivity virus r2 rmsd fails_max_frac_infectivity_at_least fails_goodness_of_fit fails_qc ignore_qc
0 A230212d0_r16 AATGACAGCTGTCTAG 1.0 A/Sydney/715/2023 0.200480 0.207000 False True True False
1 A230212d0_r16 AGGGACTTTATTGTCC 1.0 A/South_Dakota/22/2023 0.438001 0.180657 False True True False
2 A230212d28_r16 AATGACAGCTGTCTAG 1.0 A/Sydney/715/2023 0.418578 0.228428 False True True False 
Curves for viruses and serum-replicates with at least one failed barcode:
No description has been provided for this image 
Dropping 0 barcode/serum-replicates for failing max_frac_infectivity_at_least=0.0: []

Dropping 3 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('AATGACAGCTGTCTAG', 'A230212d0_r16'), ('AGGGACTTTATTGTCC', 'A230212d0_r16'), ('AATGACAGCTGTCTAG', 'A230212d28_r16')]

Fit neutralization curves after applying QC¶

No we re-fit curves after applying all the QC:

In [20]:
fits_qc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum",
    replicate_col="plate_barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

fit_params_qc = fits_qc.fitParams(average_only=False, no_average=True)
assert len(fit_params_qc) <= len(
    fits_noqc.fitParams(average_only=False, no_average=True)
)

print(f"Assigning fits for this plate to {group}")
fit_params_qc.insert(0, "group", group)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: divide by zero encountered in divide
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)

Assigning fits for this plate to pilot

Plot all the curves that passed QC:

In [21]:
if fits_qc.sera:
    _ = fits_qc.plotReplicates(
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=10,
        ticksize=10,
        ncol=6,
        draw_in_bounds=True,
    )
else:
    print("No sera passed QC.")
No description has been provided for this image

Save results to files¶

In [22]:
print(f"Writing fraction infectivities to {frac_infectivity_csv}")
(
    frac_infectivity[
        [
            "serum",
            "strain",
            "plate_barcode",
            "dilution_factor",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
    .sort_values(["serum", "plate_barcode", "dilution_factor"])
    .to_csv(frac_infectivity_csv, index=False, float_format="%.4g")
)

print(f"\nWriting fit parameters to {fits_csv}")
(
    fit_params_qc.drop(columns=["nreplicates", "ic50_str"]).to_csv(
        fits_csv, index=False, float_format="%.4g"
    )
)

print(f"\nPickling neutcurve.CurveFits object for these data to {fits_pickle}")
with open(fits_pickle, "wb") as f:
    pickle.dump(fits_qc, f)

print(f"\nWriting QC drops to {qc_drops_yaml}")


def tup_to_str(x):
    return " ".join(x) if isinstance(x, tuple) else x


qc_drops_for_yaml = {
    key: {tup_to_str(key2): val2 for key2, val2 in val.items()}
    for key, val in qc_drops.items()
}
with open(qc_drops_yaml, "w") as f:
    yaml.YAML(typ="rt").dump(qc_drops_for_yaml, f)
print("\nHere are the QC drops:\n***************************")
yaml.YAML(typ="rt").dump(qc_drops_for_yaml, sys.stdout)

Writing fraction infectivities to results/plates/plate2/frac_infectivity.csv

Writing fit parameters to results/plates/plate2/curvefits.csv

Pickling neutcurve.CurveFits object for these data to results/plates/plate2/curvefits.pickle

Writing QC drops to results/plates/plate2/qc_drops.yml

Here are the QC drops:
***************************
wells: {}
barcodes:
  AAAGTAGCAGAGGATT: min_neut_standard_frac_per_well
  AAATTCACAATATCCA: min_neut_standard_frac_per_well
  AGACCATCGCACCCAA: min_neut_standard_frac_per_well
  ATAACGTTTGTGCAAA: min_neut_standard_frac_per_well
  CAAAAGCAGCACGATA: min_neut_standard_frac_per_well
  CATAAAAGACTGTATA: min_neut_standard_frac_per_well
  CGTACGTATGTCCCAG: min_neut_standard_frac_per_well
  CGTCCCTGGCGTGTCG: min_neut_standard_frac_per_well
  CGTTAACGGCCTATCC: min_neut_standard_frac_per_well
  TATATGGAATACTAAA: min_neut_standard_frac_per_well
  TCTCCGATAGCCCTAC: min_neut_standard_frac_per_well
  TGTTGTAATCTGAATA: min_neut_standard_frac_per_well
barcode_wells:
  GCTAATTCCAAAAGCG D2: max_frac_infectivity_per_viral_barcode_well
  TAGTTGCCCCGACCTG D2: max_frac_infectivity_per_viral_barcode_well
  TGATCCGCAAGCTTAG D2: max_frac_infectivity_per_viral_barcode_well
  GCTAATTCCAAAAGCG D3: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA D3: max_frac_infectivity_per_viral_barcode_well
  CCAATAAAATACGATG D3: max_frac_infectivity_per_viral_barcode_well
  TTAGTCATCTGGGTGC D3: max_frac_infectivity_per_viral_barcode_well
  CCCCAGGTATAAAATA D3: max_frac_infectivity_per_viral_barcode_well
  ACTGAACAGTATAACT D3: max_frac_infectivity_per_viral_barcode_well
  TATCATTTCATCTACA D3: max_frac_infectivity_per_viral_barcode_well
  GTTATTATGACTTCAT D3: max_frac_infectivity_per_viral_barcode_well
  GAAAATCGAGCTTTAA D3: max_frac_infectivity_per_viral_barcode_well
  AAACTTCGTGGTATAC D3: max_frac_infectivity_per_viral_barcode_well
  AAAGATAAATTCAAAA D3: max_frac_infectivity_per_viral_barcode_well
  AACGACACTTACATCC D3: max_frac_infectivity_per_viral_barcode_well
  CTATCTTAATCTACAG D3: max_frac_infectivity_per_viral_barcode_well
  TCCAAACAGCGTTAAA D3: max_frac_infectivity_per_viral_barcode_well
  AATATACCGGCACTAC D3: max_frac_infectivity_per_viral_barcode_well
  TCAATGAATGCGGGGT D3: max_frac_infectivity_per_viral_barcode_well
  AGATCATAAGCAATAA D3: max_frac_infectivity_per_viral_barcode_well
  CCACGTTCATTAGATG D3: max_frac_infectivity_per_viral_barcode_well
  TATCGCAATATGATAA D3: max_frac_infectivity_per_viral_barcode_well
  CGCAAGGGATACTAAC D3: max_frac_infectivity_per_viral_barcode_well
  CGTCAGAAGTTTATAA D3: max_frac_infectivity_per_viral_barcode_well
  TCTTAACTACCCGATG D3: max_frac_infectivity_per_viral_barcode_well
  TTATGTTTTAATGGTA D3: max_frac_infectivity_per_viral_barcode_well
  AACAATTAATTTTTCA D3: max_frac_infectivity_per_viral_barcode_well
  TAGTTGCCCCGACCTG D3: max_frac_infectivity_per_viral_barcode_well
  GCTGGTGCACAAGATT D3: max_frac_infectivity_per_viral_barcode_well
  TGACAAACACCTGAGG D3: max_frac_infectivity_per_viral_barcode_well
  CAGCCGCTAAAATGAT D3: max_frac_infectivity_per_viral_barcode_well
  CCGCAATGACAATTTG D3: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG D3: max_frac_infectivity_per_viral_barcode_well
  CCAGAGACACGCTAGG D3: max_frac_infectivity_per_viral_barcode_well
  CCCGCTAACCCTGTCT D3: max_frac_infectivity_per_viral_barcode_well
  GCTAATTCCAAAAGCG D4: max_frac_infectivity_per_viral_barcode_well
  CCAATAAAATACGATG D4: max_frac_infectivity_per_viral_barcode_well
  CCACGTTCATTAGATG D4: max_frac_infectivity_per_viral_barcode_well
  AAACTTCGTGGTATAC D4: max_frac_infectivity_per_viral_barcode_well
  TTACGTCAATGTTTGA D4: max_frac_infectivity_per_viral_barcode_well
  AACAATTAATTTTTCA D4: max_frac_infectivity_per_viral_barcode_well
  CAAAAAGCTAATAAGT D4: max_frac_infectivity_per_viral_barcode_well
  TCGCGGTAGATTTGCG D4: max_frac_infectivity_per_viral_barcode_well
  TCTTAACTACCCGATG D4: max_frac_infectivity_per_viral_barcode_well
  CTCCAATAGGAGACGA D4: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG D4: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG D10: max_frac_infectivity_per_viral_barcode_well
  GCTAATTCCAAAAGCG G2: max_frac_infectivity_per_viral_barcode_well
  GCATTATAATCTTGTG G2: max_frac_infectivity_per_viral_barcode_well
  TACCATTTTGGTCCGC G2: max_frac_infectivity_per_viral_barcode_well
  CCAATAAAATACGATG G2: max_frac_infectivity_per_viral_barcode_well
  TCTTAACTACCCGATG G2: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG G2: max_frac_infectivity_per_viral_barcode_well
  TAAAAGAATGATGGTC G3: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA G3: max_frac_infectivity_per_viral_barcode_well
  ATCGAAAAAACTGCAA G3: max_frac_infectivity_per_viral_barcode_well
  TGTAGTATAAGAATAA G3: max_frac_infectivity_per_viral_barcode_well
  GCATTATAATCTTGTG G3: max_frac_infectivity_per_viral_barcode_well
  TATCAATTCGGTATTA G3: max_frac_infectivity_per_viral_barcode_well
  TACCATTTTGGTCCGC G3: max_frac_infectivity_per_viral_barcode_well
  TTGCTAGTCTACCTGA G3: max_frac_infectivity_per_viral_barcode_well
  GCTAATTCCAAAAGCG G3: max_frac_infectivity_per_viral_barcode_well
  AGTTATGTAAAACGTG G3: max_frac_infectivity_per_viral_barcode_well
  TTAGTCATCTGGGTGC G3: max_frac_infectivity_per_viral_barcode_well
  CCAATAAAATACGATG G3: max_frac_infectivity_per_viral_barcode_well
  GAAAATCGAGCTTTAA G3: max_frac_infectivity_per_viral_barcode_well
  AAAGATAAATTCAAAA G3: max_frac_infectivity_per_viral_barcode_well
  GTTATTATGACTTCAT G3: max_frac_infectivity_per_viral_barcode_well
  TATCATTTCATCTACA G3: max_frac_infectivity_per_viral_barcode_well
  ACGTAAATCCCCACAA G3: max_frac_infectivity_per_viral_barcode_well
  CCACAAGTTTGAAAAC G3: max_frac_infectivity_per_viral_barcode_well
  TAGCTGGGCAAAGGCT G3: max_frac_infectivity_per_viral_barcode_well
  TCAAACTATGATATTC G3: max_frac_infectivity_per_viral_barcode_well
  CTATCTTAATCTACAG G3: max_frac_infectivity_per_viral_barcode_well
  CAAGAAATGTAGTGAA G3: max_frac_infectivity_per_viral_barcode_well
  AAACTTCGTGGTATAC G3: max_frac_infectivity_per_viral_barcode_well
  TCCAAACAGCGTTAAA G3: max_frac_infectivity_per_viral_barcode_well
  GACAGAAACAAAATTA G3: max_frac_infectivity_per_viral_barcode_well
  TGCCGATCCAATTGAT G3: max_frac_infectivity_per_viral_barcode_well
  AATATACCGGCACTAC G3: max_frac_infectivity_per_viral_barcode_well
  TATCGCAATATGATAA G3: max_frac_infectivity_per_viral_barcode_well
  CCATCACCTTATACAC G3: max_frac_infectivity_per_viral_barcode_well
  CCACGTTCATTAGATG G3: max_frac_infectivity_per_viral_barcode_well
  CAAGACAAGCCCTATA G3: max_frac_infectivity_per_viral_barcode_well
  CGCAAGGGATACTAAC G3: max_frac_infectivity_per_viral_barcode_well
  TCTTAACTACCCGATG G3: max_frac_infectivity_per_viral_barcode_well
  TAGATAATAAGATTCA G3: max_frac_infectivity_per_viral_barcode_well
  TGGCTAGCGCACACCA G3: max_frac_infectivity_per_viral_barcode_well
  AACAATTAATTTTTCA G3: max_frac_infectivity_per_viral_barcode_well
  TCTTGAATTTCATGGA G3: max_frac_infectivity_per_viral_barcode_well
  CGTCAGAAGTTTATAA G3: max_frac_infectivity_per_viral_barcode_well
  ATGGTTATCTTACCTT G3: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG G3: max_frac_infectivity_per_viral_barcode_well
  CATAATGCACAAACGC G3: max_frac_infectivity_per_viral_barcode_well
  TAGTTGCCCCGACCTG G3: max_frac_infectivity_per_viral_barcode_well
  GCTGGTGCACAAGATT G3: max_frac_infectivity_per_viral_barcode_well
  CCGCAATGACAATTTG G3: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC G3: max_frac_infectivity_per_viral_barcode_well
  TAAAAGAATGATGGTC G4: max_frac_infectivity_per_viral_barcode_well
  ATCGAAAAAACTGCAA G4: max_frac_infectivity_per_viral_barcode_well
  GCATTATAATCTTGTG G4: max_frac_infectivity_per_viral_barcode_well
  AGTTATGTAAAACGTG G4: max_frac_infectivity_per_viral_barcode_well
  TACCATTTTGGTCCGC G4: max_frac_infectivity_per_viral_barcode_well
  TTAGTCATCTGGGTGC G4: max_frac_infectivity_per_viral_barcode_well
  TATTATCTAAACGGCG G4: max_frac_infectivity_per_viral_barcode_well
  GCTAATTCCAAAAGCG G4: max_frac_infectivity_per_viral_barcode_well
  GTTATTATGACTTCAT G4: max_frac_infectivity_per_viral_barcode_well
  GACCAAAAAGCAGTAT G4: max_frac_infectivity_per_viral_barcode_well
  CCCCAGGTATAAAATA G4: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA G4: max_frac_infectivity_per_viral_barcode_well
  GAAAATCGAGCTTTAA G4: max_frac_infectivity_per_viral_barcode_well
  CCAATAAAATACGATG G4: max_frac_infectivity_per_viral_barcode_well
  ACTGAACAGTATAACT G4: max_frac_infectivity_per_viral_barcode_well
  CAAGAAATGTAGTGAA G4: max_frac_infectivity_per_viral_barcode_well
  TAGCTGGGCAAAGGCT G4: max_frac_infectivity_per_viral_barcode_well
  TATCATTTCATCTACA G4: max_frac_infectivity_per_viral_barcode_well
  TCCAAACAGCGTTAAA G4: max_frac_infectivity_per_viral_barcode_well
  TCAAACTATGATATTC G4: max_frac_infectivity_per_viral_barcode_well
  AAACTTCGTGGTATAC G4: max_frac_infectivity_per_viral_barcode_well
  CCGATAAGACGTCGCT G4: max_frac_infectivity_per_viral_barcode_well
  CCACGTTCATTAGATG G4: max_frac_infectivity_per_viral_barcode_well
  CTATCTTAATCTACAG G4: max_frac_infectivity_per_viral_barcode_well
  TCAATGAATGCGGGGT G4: max_frac_infectivity_per_viral_barcode_well
  CGCAAGGGATACTAAC G4: max_frac_infectivity_per_viral_barcode_well
  TATCGCAATATGATAA G4: max_frac_infectivity_per_viral_barcode_well
  CTAAGGGCCTGTTCTT G4: max_frac_infectivity_per_viral_barcode_well
  AAGCGGTTTAGGTCCA G4: max_frac_infectivity_per_viral_barcode_well
  AATATACCGGCACTAC G4: max_frac_infectivity_per_viral_barcode_well
  CTGCGAATATTGTGAC G4: max_frac_infectivity_per_viral_barcode_well
  TAGATAATAAGATTCA G4: max_frac_infectivity_per_viral_barcode_well
  AGCATGAGCTTGTCAT G4: max_frac_infectivity_per_viral_barcode_well
  CAAAAAGCTAATAAGT G4: max_frac_infectivity_per_viral_barcode_well
  TCGCGGTAGATTTGCG G4: max_frac_infectivity_per_viral_barcode_well
  TAGTTGCCCCGACCTG G4: max_frac_infectivity_per_viral_barcode_well
  TCTTGAATTTCATGGA G4: max_frac_infectivity_per_viral_barcode_well
  TGGCTAGCGCACACCA G4: max_frac_infectivity_per_viral_barcode_well
  ATGGTTATCTTACCTT G4: max_frac_infectivity_per_viral_barcode_well
  GGTTGCGTAGTTAATC G4: max_frac_infectivity_per_viral_barcode_well
  CGTCAGAAGTTTATAA G4: max_frac_infectivity_per_viral_barcode_well
  TCTTAACTACCCGATG G4: max_frac_infectivity_per_viral_barcode_well
  AACAATTAATTTTTCA G4: max_frac_infectivity_per_viral_barcode_well
  ATCGATTCGATTGACG G4: max_frac_infectivity_per_viral_barcode_well
  GACCTCCTGGGCACGC G4: max_frac_infectivity_per_viral_barcode_well
  GCTGGTGCACAAGATT G4: max_frac_infectivity_per_viral_barcode_well
  CTCATTACAGAAATTG G4: max_frac_infectivity_per_viral_barcode_well
  TTATAATGGCCGGTAT G4: max_frac_infectivity_per_viral_barcode_well
  CCAGAGACACGCTAGG G4: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG G4: max_frac_infectivity_per_viral_barcode_well
  CCTATAAGGCCTTACG G4: max_frac_infectivity_per_viral_barcode_well
  GCATTATAATCTTGTG G5: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA G5: max_frac_infectivity_per_viral_barcode_well
  TTAGTCATCTGGGTGC G5: max_frac_infectivity_per_viral_barcode_well
  TATTATCTAAACGGCG G5: max_frac_infectivity_per_viral_barcode_well
  AAGCTAATCGTAGTCC G5: max_frac_infectivity_per_viral_barcode_well
  GCTAATTCCAAAAGCG G5: max_frac_infectivity_per_viral_barcode_well
  CAAGAAATGTAGTGAA G5: max_frac_infectivity_per_viral_barcode_well
  CCAATAAAATACGATG G5: max_frac_infectivity_per_viral_barcode_well
  TAATAACTTGAGATTC G5: max_frac_infectivity_per_viral_barcode_well
  AACGACACTTACATCC G5: max_frac_infectivity_per_viral_barcode_well
  AATATACCGGCACTAC G5: max_frac_infectivity_per_viral_barcode_well
  TCAATGAATGCGGGGT G5: max_frac_infectivity_per_viral_barcode_well
  CCACGTTCATTAGATG G5: max_frac_infectivity_per_viral_barcode_well
  AAACTTCGTGGTATAC G5: max_frac_infectivity_per_viral_barcode_well
  TATCGCAATATGATAA G5: max_frac_infectivity_per_viral_barcode_well
  TAGATAATAAGATTCA G5: max_frac_infectivity_per_viral_barcode_well
  TCTTGAATTTCATGGA G5: max_frac_infectivity_per_viral_barcode_well
  AAGAAATTATGGCAGG G5: max_frac_infectivity_per_viral_barcode_well
  CGCAAGGGATACTAAC G5: max_frac_infectivity_per_viral_barcode_well
  CTGCGAATATTGTGAC G5: max_frac_infectivity_per_viral_barcode_well
  TGGCTAGCGCACACCA G5: max_frac_infectivity_per_viral_barcode_well
  AACAATTAATTTTTCA G5: max_frac_infectivity_per_viral_barcode_well
  TAGTTGCCCCGACCTG G5: 
max_frac_infectivity_per_viral_barcode_well
  GACCTCCTGGGCACGC G5: max_frac_infectivity_per_viral_barcode_well
  CCCGCTAACCCTGTCT G5: max_frac_infectivity_per_viral_barcode_well
  GCTAATTCCAAAAGCG G6: max_frac_infectivity_per_viral_barcode_well
  CGCAAGGGATACTAAC G6: max_frac_infectivity_per_viral_barcode_well
  TCTTAACTACCCGATG G6: max_frac_infectivity_per_viral_barcode_well
  TAGTTGCCCCGACCTG G6: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG G6: max_frac_infectivity_per_viral_barcode_well
  AATGACAGCTGTCTAG G9: max_frac_infectivity_per_viral_barcode_well
barcode_serum_replicates:
  AATGACAGCTGTCTAG A230212d0_r16: goodness_of_fit
  AGGGACTTTATTGTCC A230212d0_r16: goodness_of_fit
  AATGACAGCTGTCTAG A230212d28_r16: goodness_of_fit
serum_replicates: {}